Multi-label Output Research Articles

Classification Strategies for Radar-Based Continuous Human Activity Recognition With Multiple Inputs and Multilabel Output

Classification Strategies for Radar-Based Continuous Human Activity Recognition With Multiple Inputs and Multilabel Output

Protein function prediction through multi-view multi-label latent tensor reconstruction

BackgroundIn last two decades, the use of high-throughput sequencing technologies has accelerated the pace of discovery of proteins. However, due to the time and resource limitations of rigorous experimental functional characterization, the functions of a vast majority of them remain unknown. As a result, computational methods offering accurate, fast and large-scale assignment of functions to new and previously unannotated proteins are sought after. Leveraging the underlying associations between the multiplicity of features that describe proteins could reveal functional insights into the diverse roles of proteins and improve performance on the automatic function prediction task.ResultsWe present GO-LTR, a multi-view multi-label prediction model that relies on a high-order tensor approximation of model weights combined with non-linear activation functions. The model is capable of learning high-order relationships between multiple input views representing the proteins and predicting high-dimensional multi-label output consisting of protein functional categories. We demonstrate the competitiveness of our method on various performance measures. Experiments show that GO-LTR learns polynomial combinations between different protein features, resulting in improved performance. Additional investigations establish GO-LTR’s practical potential in assigning functions to proteins under diverse challenging scenarios: very low sequence similarity to previously observed sequences, rarely observed and highly specific terms in the gene ontology.ImplementationThe code and data used for training GO-LTR is available at https://github.com/aalto-ics-kepaco/GO-LTR-prediction.

Label-Aware Dual Graph Neural Networks for Multi-Label Fundus Image Classification.

Fundus disease is a complex and universal disease involving a variety of pathologies. Its early diagnosis using fundus images can effectively prevent further diseases and provide targeted treatment plans for patients. Recent deep learning models for classification of this disease are gradually emerging as a critical research field, which is attracting widespread attention. However, in practice, most of the existing methods only focus on local visual cues of a single image, and ignore the underlying explicit interaction similarity between subjects and correlation information among pathologies in fundus diseases. In this paper, we propose a novel label-aware dual graph neural networks for multi-label fundus image classification that consists of population-based graph representation learning and pathology-based graph representation learning modules. Specifically, we first construct a population-based graph by integrating image features and non-image information to learn patient's representations by incorporating associations between subjects. Then, we represent pathologies as a sparse graph where its nodes are associated with pathology-based feature vectors and the edges correspond to probability of the co-occurrence of labels to generate a set of classifier scores by the propagation of multi-layer graph information. Finally, our model can adaptively recalibrate multi-label outputs. Detailed experiments and analysis of our results show the effectiveness of our method compared with state-of-the-art multi-label fundus image classification methods.

Initial Progress of Identification of the Appropriate NLP Technique for Content Evaluation in Textual Conversations of People Infected by Sars-Cov-2

When Covid-19 became a pandemic on March 2020, an urgent need arose for reliable info and advice, so Virtual Assistants were created to help teach the public how to avoid the Alpha variant. But when new variants like Beta, Delta, and Omicron appeared with different symptoms, they caused new waves of infections and deaths. To tackle this, a Natural Language Processing prototype was created to analyze experiences of 4422 people, who had been infected in Ecuador, and to detect which symptoms were most common in their conversations. This study prompted the creation of the NLP prototype, using Python language, the Google Collab platform, two combinations of NLP techniques were considered, measuring results through quality metrics, accuracy, Recall, F1, finding that the most appropriate combination of techniques of the two tested the one that gave the highest effectiveness for a Multi-Label classifier model, including Stop Word, Tokenization, Stemming with LSTM (Long Short-Term Memory) classifier, as a first advance of the study.

Exploiting local label correlation from sample perspective for multi-label classification via three-way decision theory

In multi-label classification, the expansion of output dimension seriously interferes learning performance, and even fails to build a joint prediction model. In order to restrain the proliferation of multi-label classifier’s hypothesis space, the current works focus on the application of global positive label correlation. However, the “black or white” mechanism ignore other possible forms of label correlation, such as negative or neutral correlation. By introducing the doctrine of the mean, three-way decision (3WD) theory provides a solution for in-depth research on local label correlation, and aims to handle the uncertainty of multi-label learning tasks. In this paper, a novel learning algorithm for multi-label joint classification, namely ML-3WD, is proposed by considering the 3WD label correlation from the perspective of samples. According to the weights of different features on any label, the comprehensive loss of each sample to three action strategies can be measured. Obviously, the 3WD rules for any label variable in multi-label output space is obtained. By aggregating the cutting thresholds between different labels, the division principles of 3WD label correlation are further established. Given any multi-label sample, the local fuzzy membership to co-occurrence or mutual state for label pair is examined based on kernelized fuzzy rough sets. The 3WD local label relevance of each sample is confirmed, that is, positive, negative or neutral. The global application strategy for multi-label classification is utilized to avoid over-fitting induced by local mining strategy. Based on the integral mean of the distribution of 3WD local label relevance in multi-label sample space, two different versions of empirical label relevance are constructed. By constraining the relative position between sub-separation hyperplanes, the 3WD label correlation distribution-based model for multi-label joint classification is designed. The experiment results on fifteen real world multi-label datasets reflect that our algorithm achieves good classification ability and versatility. The impact of core parameters on learning performance is also dissected.

Predicting Activities of Daily Living for the Coming Time Period in Smart Homes

Activity prediction aims to predict what activities will occur in the future. In smart homes, to facilitate the daily living of the residents, automated or assistive services are provided. To provide these services, the ability of activity prediction is necessary. When we make a prediction, most of the existing works focus on predicting information about the next activity. However, in a smart home environment, compared with just predicting information about the next activity, another type of activity prediction problem has more practical value: predicting what activities will occur in the coming time period of a certain length. The necessity of this type of prediction is due to the purpose of the smart homes and the character of the activities. Many activities in smart homes need preparation time before being performed. Through this type of prediction, activities could be predicted sufficient time before being performed, and there will be adequate time for the smart home system to prepare corresponding automated or assistive services. As more than one activity could occur within the time period in which the prediction is made, this problem is a multilabel classification problem. In this article, we first give a formulation of the problem. Then, we propose a deep learning model to address it. The proposed model consists of the convolutional part, the long short-term memory layer, and the multilabel output layer. Experiments on real-world datasets show the effectiveness of our model.

Hierarchical multi-label taxonomic classification of carbonate skeletal grains with deep learning

Recent advances in Artificial Intelligence (AI), particularly the rise of deep learning, are revolutionizing data collection and analysis in many aspects of the Earth Sciences, including paleontology. Rapid digital transformation of paleontological information enables automation of various paleontological tasks using AI technology. One such task is identifying and classifying skeletal grains at different levels of the Linnaean taxonomic hierarchy, both macro- and microscopically. Unfortunately, fossil classification remains largely untouched by AI due to the expertise and time required to generate high-quality, large-scale labeled training data. This task is particularly challenging when part of the compositional analysis of limestone, where three-dimensional objects (i.e., skeletal grains) are observed in a two-dimensional cross-section. It is compounded by the imbalanced data and limited number of images available, approximately four orders smaller than the number necessary to train deep neural networks. Recent efforts to classify such fossil images using deep learning returned a single output/label classification, limiting the information output from the model, and hindering its further applications to replicate paleontologist-level identification. Here, we couple a multi-head deep convolutional neural network architecture called TaxonNet that adopts the Branch Convolutional Neural Networks and advanced image augmentation to overcome these barriers. This model uses prior knowledge of hierarchical category relationships to output multiple hierarchical taxonomic predictions from a single petrographic image. In this study, we developed and evaluated four different strategies of hierarchical classification: (i) forward (coarse to fine); (ii) reverse (fine to coarse); (iii) parallel (coarse and fine simultaneously), and (iv) hybrid. The forward and reverse strategies yield the most accurate prediction of ~95 % accuracy for coarse-level and fine-level recognitions across major fossil types in carbonate petrographic images, including algae, mollusks, and corals. These results represent an important step toward applying AI to advance fossil grain classification and exhibit a promising real-world geological application of a hierarchical network in paleontology, sedimentary petrography, and other image-based geological analysis requiring hierarchical, multilabel output.

Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data

Prediction of survival for patients in intensive care units (ICUs) has been subject to intense research. However, no models exist that embrace the multiverse of data in ICUs. It is an open question whether deep learning methods using automated data integration with minimal pre-processing of mixed data domains such as free text, medical history and high-frequency data can provide discrete-time survival estimates for individual ICU patients. We trained a deep learning model on data from patients admitted to ten ICUs in the Capital Region of Denmark and the Region of Southern Denmark between 2011 and 2018. Inspired by natural language processing we mapped the electronic patient record data to an embedded representation and fed the data to a recurrent neural network with a multi-label output layer representing the chance of survival at different follow-up times. We evaluated the performance using the time-dependent concordance index. In addition, we quantified and visualized the drivers of survival predictions using the SHAP methodology. We included 37,355 admissions of 29,417 patients in our study. Our deep learning models outperformed traditional Cox proportional-hazard models with concordance index in the ranges 0.72–0.73, 0.71–0.72, 0.71, and 0.69–0.70, for models applied at baseline 0, 24, 48, and 72 h, respectively. Deep learning models based on a combination of entity embeddings and survival modelling is a feasible approach to obtain individualized survival estimates in data-rich settings such as the ICU. The interpretable nature of the models enables us to understand the impact of the different data domains.

Deformable Convolutional Networks for Efficient Mixed-Type Wafer Defect Pattern Recognition

Defect pattern recognition (DPR) of wafer maps is critical for determining the root cause of production defects, which can provide insights for the yield improvement in wafer foundries. During wafer fabrication, several types of defects can be coupled together in a piece of wafer, it is called mixed-type defects DPR. To detect mixed-type defects is much more complicated because the combination of defects may vary a lot, from the type of defects, position, angle, number of defects, etc. Deep learning methods have been a good choice for complex pattern recognition problems. In this article, we propose a deformable convolutional network (DC-Net) for mixed-type DPR (MDPR) in which several types of defects are coupled together in a piece of wafer. A deformable convolutional unit is designed to selectively sample from mixed defects, then extract high-quality features from wafer maps. A multi-label output layer is improved with a one-hot encoding mechanism, which decomposes extract mixed features into each basic single defect. The experiment results indicate that the proposed DC-Net model outperforms conventional models and other deep learning models. Further results of the interpretable analysis reveal that the proposed DC-Net can accurately pinpoint the defects areas of wafer maps with noise points, which is beneficial for mixed-type DPR problems.

Label Co-Occurrence Learning With Graph Convolutional Networks for Multi-Label Chest X-Ray Image Classification

Existing multi-label medical image learning tasks generally contain rich relationship information among pathologies such as label co-occurrence and interdependency, which is of great importance for assisting in clinical diagnosis and can be represented as the graph-structured data. However, most state-of-the-art works only focus on regression from the input to the binary labels, failing to make full use of such valuable graph-structured information due to the complexity of graph data. In this paper, we propose a novel label co-occurrence learning framework based on Graph Convolution Networks (GCNs) to explicitly explore the dependencies between pathologies for the multi-label chest X-ray (CXR) image classification task, which we term the "CheXGCN". Specifically, the proposed CheXGCN consists of two modules, i.e., the image feature embedding (IFE) module and label co-occurrence learning (LCL) module. Thanks to the LCL model, the relationship between pathologies is generalized into a set of classifier scores by introducing the word embedding of pathologies and multi-layer graph information propagation. During end-to-end training, it can be flexibly integrated into the IFE module and then adaptively recalibrate multi-label outputs with these scores. Extensive experiments on the ChestX-Ray14 and CheXpert datasets have demonstrated the effectiveness of CheXGCN as compared with the state-of-the-art baselines.

Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning

In existing visual representation learning tasks, deep convolutional neural networks (CNNs) are often trained on images annotated with single tags, such as ImageNet. However, a single tag cannot describe all important contents of one image, and some useful visual information may be wasted during training. In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model. To this end, we build a large-scale multi-label image database with 18M images and 11K categories, dubbed Tencent ML-Images. We efficiently train the ResNet-101 model with multi-label outputs on Tencent ML-Images, taking 90 hours for 60 epochs, based on a large-scale distributed deep learning framework,i.e.,TFplus. The good quality of the visual representation of the Tencent ML-Images checkpoint is verified through three transfer learning tasks, including single-label image classification on ImageNet and Caltech-256, object detection on PASCAL VOC 2007, and semantic segmentation on PASCAL VOC 2012. The Tencent ML-Images database, the checkpoints of ResNet-101, and all the training codehave been released at https://github.com/Tencent/tencent-ml-images. It is expected to promote other vision tasks in the research and industry community.

Rectified-Linear-Unit-Based Deep Learning for Biomedical Multi-label Data.

Disease diagnosis is one of the major data mining questions by the clinicians. The current diagnosis models usually have a strong assumption that one patient has only one disease, i.e. a single-label data mining problem. But the patients, especially when at the late stages, may have more than one disease and require a multi-label diagnosis. The multi-label data mining is much more difficult than a single-label one, and very few algorithms have been developed for this situation. Deep learning is a data mining algorithm with highly dense inner structure and has achieved many successful applications in the other areas. We propose a hypothesis that rectified-linear-unit-based deep learning algorithm may also be good at the clinical questions, by revising the last layer as a multi-label output. The proof-of-concept experimental data support the hypothesis, and the community may be interested in trying more applications.

Error-correcting output codes for multi-label emotion classification

Multi-modal affective data such as EEG and physiological signals is increasingly utilized to analyze of human emotional states. Due to the noise existed in collected affective data, however, the performance of emotion recognition is still not satisfied. In fact, the issue of emotion recognition can be regarded as channel coding, which focuses on reliable communication through noise channels. Using affective data and its label, the redundant codeword would be generated to correct signals noise and recover emotional label information. Therefore, we utilize multi-label output codes method to improve accuracy and robustness of multi-dimensional emotion recognition by training a redundant codeword model, which is the idea of error-correcting output codes. The experiment results on DEAP dataset show that the multi-label output codes method outperforms other traditional machine learning or pattern recognition methods for the prediction of emotional multi-labels.

Reduction of false positives in lung nodule detection using a two-level neural classification

The authors have developed a neural-digital computer-aided diagnosis system, based on a parameterized two-level convolution neural network (CNN) architecture and on a special multilabel output encoding procedure. The developed architecture was trained, tested, and evaluated specifically on the problem of diagnosis of lung cancer nodules found on digitized chest radiographs. The system performs automatic "suspect" localization, feature extraction, and diagnosis of a particular pattern-class aimed at a high degree of "true-positive fraction" detection and low "false-positive fraction" detection. In this paper, the authors aim at the presentation of the two-level neural classification method in reducing false-positives in their system. They employed receiver operating characteristics (ROC) method with the area under the ROC curve (A(z)) as the performance index to evaluate all the simulation results. The two-level CNN showed superior performance (A(z)=0.93) to the single-level CNN (A(z)=0.85). The proposed two-level CNN architecture is proven to be promising and to be extensible, problem-independent, and therefore, applicable to other medical or difficult diagnostic tasks in two-dimensional (2-D) image environments.